A window into fungal endophytism in Salicornia europaea: deciphering fungal characteristics as plant growth promoting agents

Aim Plant-endophytic associations exist only when equilibrium is maintained between both partners. This study analyses the properties of endophytic fungi inhabiting a halophyte growing in high soil salinity and tests whether these fungi are beneficial or detrimental when non-host plants are inoculated. Method Fungi were isolated from Salicornia europaea collected from two sites differing in salinization history (anthropogenic and naturally saline) and analyzed for plant growth promoting abilities and non-host plant interactions. Results Most isolated fungi belonged to Ascomycota (96%) including dematiaceous fungi and commonly known plant pathogens and saprobes. The strains were metabolically active for siderophores, polyamines and indole-3-acetic acid (mainly Aureobasidium sp.) with very low activity for phosphatases. Many showed proteolytic, lipolytic, chitinolytic, cellulolytic and amylolytic activities but low pectolytic activity. Different activities between similar fungal species found in both sites were particularly seen for Epiccocum sp., Arthrinium sp. and Trichoderma sp. Inoculating the non-host Lolium perenne with selected fungi increased plant growth, mainly in the symbiont (Epichloë)-free variety. Arthrinium gamsii CR1-9 and Stereum gausapatum ISK3-11 were most effective for plant growth promotion. Conclusions This research suggests that host lifestyle and soil characteristics have a strong effect on endophytic fungi, and environmental stress could disturb the plant-fungi relations. In favourable conditions, these fungi may be effective in facilitating crop production in non-cultivable saline lands

BORDERLINE LEPROMATOUS LEPROSY WITH NEUROFIBROMATOSIS

The coexistence of leprosy with neurofibromatosis is rare both the diseases present with nerve thickening and skin lesions (patches and nodules). The coexistence of neurofibroma with borderline tuberculoid, lepromatous, histoid, and neuritic leprosy has been reported in the past. We report here a case of borderline lepromatous leprosy coexisting with neurofibromatosis in a 60 year-old male, who presented with neurofibromata and nerve thickening. Histopathology of skin biopsy from the leprosy and neurofibroma nodules confirmed the diagnosis of leprosy and neurofibroma

Review: machine learning techniques applied to cybersecurity

Machine learning techniques are a set of mathematical models to solve high non-linearity problems of different topics: prediction, classification, data association, data conceptualization. In this work, the authors review the applications of machine learning techniques in the field of cybersecurity describing before the different classifications of the models based on (1) their structure, network-based or not, (2) their learning process, supervised or unsupervised and (3) their complexity. All the capabilities of machine learning techniques are to be regarded, but authors focus on prediction and classification, highlighting the possibilities of improving the models in order to minimize the error rates in the applications developed and available in the literature. This work presents the importance of different error criteria as the confusion matrix or mean absolute error in classification problems, and relative error in regression problems. Furthermore, special attention is paid to the application of the models in this review work. There are a wide variety of possibilities, applying these models to intrusion detection, or to detection and classification of attacks, to name a few. However, other important and innovative applications in the field of cybersecurity are presented. This work should serve as a guide for new researchers and those who want to immerse themselves in the field of machine learning techniques within cybersecurity